Data Gaps Research Articles

Potential Environmental Impacts of Coastal Desalination Intake Structures: Urgent Data Gaps and Policy Needs

Potential Environmental Impacts of Coastal Desalination Intake Structures: Urgent Data Gaps and Policy Needs

Proposals for new transfer coefficient (TC) values for worker re-entry activities in grape vineyards

New transfer coefficient (TC) values were derived for vineyard workers handling treated grapevines during harvesting and crop maintenance activities. Re-entry exposure and dislodgeable foliar residue (DFR) studies were performed in Europe, covering hand harvesting, pruning/training, pruning/tying and pruning /shoot lifting. Foliar applications of fungicides (iprovalicarb, dimethomorph, dithianon, pyrimethanil and fenbuconazole) were made and 73 workers at 16 sites were monitored over one working day. Exposure was measured on inner and outer dosimeters, face/neck wipes and hand washes. In concurrent DFR studies, leaf punches were taken at each site during the time of worker re-entry. Potential exposure values correlated well with DFR values. TC values were derived for various re-entry activities for potential and actual exposure, with and without gloves. The harvesting task resulted in lower TC values than the other crop maintenance tasks. Additional TC values reflecting the use of protective gloves can be derived from the results. The TC values are much lower than current European Food Safety Authority (EFSA) default values. This project addresses a data gap identified by EFSA for specific EU TC values to permit more realistic and reliable re-entry worker exposure estimates for grapes.

Particle analysis of surgical lung biopsies from deployed and non-deployed US service members during the Global War on Terrorism.

The role that inhaled particulate matter plays in the development of post-deployment lung disease among US service members deployed to Southwest Asia during the Global War on Terrorism has been difficult to define. There is a persistent gap in data addressing the relationship between relatively short-term (months to a few years) exposures to high levels of particulate matter during deployment and the subsequent development of adverse pulmonary outcomes. Surgical lung biopsies from deployed service members and veterans (DSMs) and non-deployed service members and veterans (NDSMs) who develop lung diseases can be analyzed to potentially identify residual deployment-specific particles and develop associations with pulmonary pathological diagnoses. We examined 52 surgical lung biopsies from 25 DSMs and 27 NDSMs using field emission scanning electron microscopy (FE-SEM) with energy dispersive x-ray spectroscopy (EDS) to identify any between-group differences in the number and composition of retained inorganic particles, then compared the particle analysis results with the original histopathologic diagnoses. We recorded a higher number of total particles in biopsies from DSMs than from NDSMs, and this difference was mainly accounted for by geologic clays (illite, kaolinite), feldspars, quartz/silica, and titanium-rich silicate mixtures. Biopsies from DSMs deployed to other Southwest Asia regions (SWA-Other) had higher particle counts than those from DSMs primarily deployed to Iraq or Afghanistan, due mainly to illite. Distinct deployment-specific particles were not identified. Particles did not qualitatively associate with country of deployment. The individual diagnoses of the DSMs and NDSMs were not associated with elevated levels of total particles, metals, cerium oxide, or titanium dioxide particles. These results support the examination of particle-related lung disease in DSMs in the context of comparison groups, such as NDSMs, to assist in determining the strength of associations between specific pulmonary pathology diagnoses and deployment-specific inorganic particulate matter exposure.

Paired metagenomic and chemical evaluation of aflatoxin-contaminated dog kibble

IntroductionIdentification of chemical toxins from complex or highly processed foods can present ‘needle in the haystack’ challenges for chemists. Metagenomic data can be used to guide chemical toxicity evaluations by providing DNA-based description of the wholistic composition (eukaryotic, bacterial, protozoal, viral, and antimicrobial resistance) of foods suspected to harbor toxins, allergens, or pathogens. This type of information can focus chemistry-based diagnostics, improve hazard characterization and risk assessment, and address data gaps. Additionally, there is increasing recognition that simultaneously co-occurring mycotoxins, either from single or multiple species, can impact dietary toxicity exposure. Metagenomic data provides a way to address data gaps related to co-occurrence of multiple fungal species.MethodsPaired metagenomic and chemical data were used to evaluate aflatoxin-contaminated kibble with known levels of specific mycotoxins. Kibble was ground to a fine powder for both chemical and molecular analyses. Chemical analyses were performed with Liquid Chromatography Mass Spectrometry (LCMS) and according to the AOAC Official method 2005.08: Aflatoxins in Corn, Raw Peanuts, and Peanut Butter using Liquid Chromatography with Post-Column Photochemical Derivatization. Metagenomes were created from DNA extracted from ground kibble and sequenced on an Illumina NextSeq 2000 with an average sequence depth of 180 million reads per replicate.Results and discussionMetagenomic data demonstrated that the abundance of DNA from putative aflatoxigenic Aspergillus spp. correlated with the levels of aflatoxin quantified by LCMS. Metagenomic data also identified an expansive range of co-occurring fungal taxa which may produce additional mycotoxins. DNA data paired with chemical data provides a novel modality to address current data gaps surrounding dietary mycotoxin exposure, toxigenic fungal taxonomy, and mycotoxins of emerging concern.

High-resolution freshwater dissolved calcium and pH data layers for Canada and the United States

Freshwater ecosystems are biologically important habitats that provide many ecosystem services. Calcium concentration and pH are two key variables that are linked to multiple chemical processes in these environments, influence the biology of organisms from diverse taxa, and can be important factors affecting the distribution of native and non-native species. However, it can be challenging to obtain high-resolution data for these variables at regional and national scales. To address this data gap, water quality data for lakes and rivers in Canada and the continental USA were compiled and used to generate high-resolution (10 × 10 km) interpolated raster layers, after comparing multiple spatial interpolation approaches. This is the first time that such data have been made available at this scale and resolution, providing a valuable resource for research, including projects evaluating risks from environmental change, pollution, and invasive species. This will aid the development of conservation and management strategies for these vital habitats.

Smoothing the Phosphorus Resource Stress under the Socioeconomic Development in China.

Phosphorus (P) is the key in maintaining food security and ecosystem functions. Population growth and economic development have increased the demand for phosphate rocks. China has gradually developed from zero phosphate mining to the world's leading P miner, fertilizer, and agricultural producer since 1949. China released policies, such as designating phosphate rock as a strategic resource, promoting eco-agricultural policies, and encouraging the use of solid wastes produced in mining and the phosphorus chemical industry as construction materials. However, methodological and data gaps remain in the mapping of the long-term effects of policies on P resource efficiency. Here, P resource efficiency can be represented by the potential of the P cycle to concentrate or dilute P as assessed by substance flow analysis (SFA) complemented by statistical entropy analysis (SEA). P-flow quantification over the past 70 years in China revealed that both resource utilization and waste generation peaked around 2015, with 20 and 11 Mt of mined and wasted P, respectively. Additionally, rapidly increasing aquaculture wastewater has exacerbated pollution. The resource efficiency of the Chinese P cycle showed a U-shaped change with an overall improvement of 22.7%, except for a temporary trough in 1975. The driving force behind the efficiency decline was the roaring phosphate fertilizer industry, as confirmed by the sharp increase in P flows for both resource utilization and waste generation from the mid-1960s to 1975. The positive driving forces behind the 30.7% efficiency increase from 1975 to 2018 were the implementation of the resource conservation policy, downstream pollution control, and, especially, the circular agro-food system strategy. However, not all current management practices improve the P resource efficiency. Mixing P industry waste with construction materials and the development of aquaculture to complement offshore fisheries erode P resource efficiency by 2.12% and 9.19%, respectively. With the promotion of a zero-waste society in China, effective P-cycle management is expected.

Sounding out horse mussel sediment thickness: an integrated data approach

Horse mussel beds are listed as a Priority Marine Feature (PMF) in Scotland for their influence in the creation of diverse benthic communities and provision of ecosystem services. In Scotland, horse mussel beds are also recognised for their importance in marine carbon sequestration. Unfortunately, there is a paucity of data on horse mussel bed carbon stocks and sediment thickness. There are also significant financial and logistical barriers with which to address these data gaps. This study investigates the robustness of Sub-Bottom Profiling (SBP) as a cost-effective method to quantify horse mussel sediment thickness across a landscape. Integrating SBP and Drop-Down Video (DDV) data, this study also details an integrated approach to investigate the links between horse mussel habitat condition and sediment thickness. With the addition of abiotic data, this study uses Structural Equation Modelling (SEM) to elucidate key relationships between abiotic factors, biotic variables, and sediment thickness. There is a significant positive correlation of horse mussel habitat condition and sediment thickness. Average horse mussel total sediment thickness, across all measures of habitat condition, was 1.37m. These findings substantially increase previous estimates of horse mussel sediment thickness, and potential value to climate change mitigation through blue carbon frameworks. This study highlights the importance of both abiotic and biotic factors on marine carbon sediment quantification.

What bycatch tells us about the diet of harbor and gray seals and overlap with commercial fishermen

Northwest Atlantic harbor (Phoca vitulina vitulina) and gray (Halichoerus grypus atlantica) seal populations are recovering from early to mid-20th century exploitation, increasing their biological interactions and bycatch in Northeastern US commercial fisheries. We evaluated the seals’ diet composition and compared their prey to commercial catches to assess trophic overlap and potential competition with commercial fisherman target catches. We obtained 148 harbor and 178 gray seal stomach samples from bycatch events that occurred between 2004 and 2018. We learned from the hard part remains that the majority of seals bycaught are young-of-the-year (≤12 months old) that consume a wide breadth of prey across three trophic groups. There was a general dichotomy in extrinsic factors associated with seal diet in which 45% trophic niche separation was explained by non-overlapping harbor and gray seal phenology and pup haul-out locations that are adjacent to active fishing areas. Prey size estimated from fish otoliths and squid beaks recovered from stomach contents showed that gray seals consumed larger prey than harbor seals and prey sizes from both seals showed limited overlap with prey sizes caught by commercial gillnet fishermen. The most important prey to both seals included large (>20 cm) and small (≤20 cm) silver hake (Merluccius bilinearis), (≤40 cm) red hake (Urophycis chuss), gulf stream flounder (Citharichthys arctifrons), medium (21–40 cm) white hake (Urophycis tenuis), and (<50 cm) Atlantic cod (Gadus morhua). Important prey to harbor seals that did not overlap with gray seals were Acadian redfish (Sebastes fasciatus), Atlantic herring (Clupea harengus), longfin (Doryteuthis pealeii), and shortfin squid (Illex illecebrosus). They contrasted with prey important to gray seals that did not overlap with harbor seals: yellowtail flounder (Limanda ferruginea), sand lance (Ammodytes spp.), Urophycis spp., and fourspot flounder (Hippoglossina oblonga). Despite the potential bias associated with opportunistic bycatch sampling, this study demonstrates the importance and value of utilizing carcasses retained from bycatch events, is complimentary to newer methodologies (i.e., DNA meta-barcoding), and fills data gaps in our understanding of the role recovering harbor and gray seal populations have on Northeastern US regional food webs.

Reliability of the picture description task of the Western Aphasia Battery – revised in Laurentian French persons without brain injury

Objective: Limited normative data (including psychometric properties) are currently available on discourse tasks in non-dominant languages such as Laurentian (Quebec) French. The lack of linguistic and cultural adaptation has been identified as a barrier to discourse assessment. The main aim of this study is to document inter-rater and test-retest reliability properties of the picnic scene of the Western Aphasia Battery – Revised (WAB-R), including the cultural adaptation of an information content unit (ICU) list, and provide a normative reference for persons without brain injury (PWBI). Method: To do so, we also aimed to adapt an ICU checklist culturally and linguistically for Laurentian French speakers. Discourse samples were collected from 66 PWBI using the picture description task of the WAB-R. The ICU list was first adapted into Laurentian French. Then, ICUs and thematic units (TUs) were extracted manually, and microstructural variables were extracted using CLAN. Inter-rater reliability and test-retest reliability were determined. Results: Excellent inter-rater reliability was obtained for ICUs and TUs, as well as for all microstructural variables, except for mean length of utterance, which was found to be good. Conversely, test-retest reliability ranged from poor to moderate for all variables. Conclusion: The present study provides a validated ICU checklist for clinicians and researchers working with Laurentian French speakers when assessing discourse with the picnic scene of the WAB-R. It also addresses the gap in available psychometric data regarding inter-rater and test-retest reliability in PWBI.

Surveying immigrant-origin voters in a post-migrant society: The first Immigrant German Election Study, 2017

This paper introduces the Immigrant German Election Study (IMGES) as the first survey that explicitly targeted immigrant-origin voters in Germany. IMGES fills the gap of insufficient data in the field of immigrant-origin voters with a combination of proven and novel survey measures of the electoral behavior of people with a background from either Türkiye or from the former Soviet Union or its successor states. The study was carried out in a post-election, cross-sectional survey in 2017. Its compatibility with the German Longitudinal Election Study (GLES), the Ethnic Minority British Election Study (EMBES), and the Dutch Ethnic Minority Election Study (DEMES) allows for in-depth comparative analyses between immigrant-origin voters and natives across different countries. In addition to standard measures of electoral behavior, the data include measures of immigrant-specific factors relevant to voting behavior. Moreover, the dataset is not limited to voting behavior in Germany, it also includes transnational voting behavior in the respective countries of origin.

Learning from local knowledge on changes in tree-cover and water availability: the case of the contested agroforested landscape of the Mt. Elgon Water Tower, Uganda

Participatory Rural Appraisal was used to compare stakeholder perceptions of spatial and temporal variation in tree cover and water availability using Uganda’s Mt. Elgon Water Tower as a case study. This study was guided by the research question: How do multi-level stakeholders’ perceptions of changes in tree-cover and water availability vary across spatial and temporal scales of the water tower? Five Participatory Rural Appraisal tools were applied to understand multi-level stakeholders’ perspectives on the changes in tree-cover and water availability. Data was collected from farmers’ focus group discussions, key experts from local government offices and structured household interviews with local communities. This study focused on the upper and lower zones of the water tower, which differ in terms of proximity to Mt. Elgon National Forest Park, household water-use, elevation, and tree-cover. Results showed that there are both similarities and differences in perceptions of changes in tree cover and water availability among stakeholders. Farmers and key experts perceived a decrease in water availability and a decrease in overall tree cover for the period 1990–2020. There are differences in stakeholders’ perceptions of water availability across the zones and sub-catchments. For instance, more farmers in Sipi River Sub-catchment perceived a decrease in water availability during 2006–2020 compared to 1990–2005 period. There were notable contradictions in farmers’ and key experts’ perceptions on tree cover. Farmers’ perceptions of 1990–2020 precipitation were in agreement with trends in existing precipitation data. Declining trends of natural tree cover especially outside the Mt. Elgon national forest park pose great threat to water resources originating from forests. Changes in tree cover and water availability in the study area can be partly explained by the tree species grown. Local stakeholders’ perceptions complemented the existing data gaps in explaining the changes in tree cover and water availability. Participatory Rural Appraisal tools are helpful for documenting the perceptions of local communities. However, this cannot replace reliable data resulting from large-scale efforts for monitoring changes and variations in tree-cover and water availability. The findings of this paper can be valuable to inform strategies for community involvement in sustainable agroforested landscape management.

Exploring a mechanism-based approach for the identification of endocrine disruptors using Adverse Outcome Pathways and New Approach Methodologies: A perfluorooctane sulfonic acid case study

Endocrine disruptors (EDs) pose a serious threat to human health and the environment and require a comprehensive evaluation to be identified. The identification of EDs require a substantial amount of data, both in vitro and in vivo, due to the current scientific criteria in the EU. At the same time, the EU strives to reduce animal testing due to concerns regarding animal welfare and sensitivity of animal studies to adequately detect adverse effects relevant for human health. Perfluorooctane sulfonic acid (PFOS) is a persistent organic pollutant that is suspected to be an ED based on academic research, however it is not identified as such from a regulatory perspective. It has previously been shown that PFOS has the potential to cause neurotoxicity as well as affect the thyroid system, and it is known that specific thyroid hormone levels are critical in the development of the brain during. In this work, the aim was to evaluate a mechanism-based approach to identify ED properties of PFOS based on the Adverse Outcome Pathway (AOP) framework and using New Approach Methods (NAMs), by comparing this approach to an ED assessment based on the currently available guidance document. An AOP network (AOPN) was generated for the thyroid modality, and AOPs leading to developmental neurotoxicity (DNT) were identified. A literature search and screening process based on the AOPN, and systematic review methodology, was performed, followed by a rigorous Weight-of-Evidence (WoE) assessment. Evidence was mapped back onto the AOPN used for the literature search, to identify possible endocrine Modes-of-Action (MoAs) for PFOS and data gaps in the two assessments. It could be concluded that PFOS fulfils the criteria for ED classification in the standard ED assessment, but not in the mechanism-based assessment. The need for quantitative information, such as quantitative AOPs, for the mechanism-based approach is discussed. The possibility of a directly neurotoxic alternative MoA was also highlighted based on available in vitro data. Opportunities and challenges with implementing AOPs and NAMs into the regulatory assessment of EDs, and assessing hazard in the Next Generation Risk Assessment, is discussed. This case study exploring the mechanism-based approach to ED identification represents an important step toward more accurate and predictive assessment of EDs based on AOPs and NAMs, and to the Next Generation Risk Assessment (NGRA) concept.

Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling

Surface water is a vital component of the Earth’s water cycle and characterizing its dynamics is essential for understanding and managing our water resources. Satellite-based remote sensing has been used to monitor surface water dynamics, but cloud cover can obscure surface observations, particularly during flood events, hindering water identification. The fusion of optical and synthetic aperture radar (SAR) data leverages the advantages of both sensors to provide accurate surface water maps while increasing the temporal density of unobstructed observations for monitoring surface water spatial dynamics. This paper presents a method for generating dense time series of surface water observations using optical–SAR sensor fusion and gap filling. We applied this method to data from the Copernicus Sentinel-1 and Landsat 8 satellite data from 2019 over six regions spanning different ecological and climatological conditions. We validated the resulting surface water maps using an independent, hand-labeled dataset and found an overall accuracy of 0.9025, with an accuracy range of 0.8656–0.9212 between the different regions. The validation showed an overall false alarm ratio (FAR) of 0.0631, a probability of detection (POD) of 0.8394, and a critical success index (CSI) of 0.8073, indicating that the method generally performs well at identifying water areas. However, it slightly underpredicts water areas with more false negatives. We found that fusing optical and SAR data for surface water mapping increased, on average, the number of observations for the regions and months validated in 2019 from 11.46 for optical and 55.35 for SAR to 64.90 using both, a 466% and 17% increase, respectively. The results show that the method can effectively fill in gaps in optical data caused by cloud cover and produce a dense time series of surface water maps. The method has the potential to improve the monitoring of surface water dynamics and support sustainable water management.

Up-bottom assessments of nutrient supply and gaseous pollutant from Chinese wheat straw field management

To achieve resource efficiency, and carbon neutrality, it is vital to evaluate nutrient supply and gaseous pollutant emissions associated with field management of bio-straw resources. Previous straw yield estimates have typically relied on a constant grain-to-straw yield ratio without accounting for grain yield levels in a given region. Addressing this high-resolution data gap, our study introduces a novel empirical model for quantifying grain-to-straw yield, which has been used to gauge wheat straw field management practices at the city level during 2011–2015. Utilizing both statistical review and GIS-based methods, average nitrogen (N), phosphorus (P), and potassium (K) supplies from straw field management stood at 1510, 1229, and 61700 tons, respectively. Average emissions of PM2.5, SO2, NOx, NH3, CH4, and CO2 due to straw burning were 367, 41, 160, 18, 165, and 70,644 tons, respectively. We also reported uncertainty from Monte Carlo model as the 5th-95th percentiles of estimated nutrient supply and gaseous pollutant. These insights will provide foundational support for the sustainable and environmentally friendly management of wheat straw in China.

Effects of Temperature and Salinity on Perfluorooctane Sulfonate (PFOS) Toxicity in Larval Estuarine Organisms

Perfluorooctane sulfonate (PFOS) is a persistent contaminant that has been found globally within the environment. Key data gaps exist in the toxicity of PFOS to marine organisms, especially estuarine species that are crucial to the food web: fish, shrimp, and mollusks. This study developed toxicity thresholds for larval estuarine species, including grass shrimp (Palaemon pugio), sheepshead minnows (Cyprinodon variegatus), mysids (Americamysis bahia), and Eastern mud snails (Tritia obsoleta). Multiple abiotic stressors (salinity and temperature) were included as variables in testing the toxicity of PFOS. Acute 96 h toxicity testing under standard test conditions of 25 °C and 20 ppt seawater yielded LC50 values of 0.919 mg/L for C. variegatus, 1.375 mg/L for A. bahia, 1.559 mg/L for T. obsoleta, and 2.011 mg/L for P. pugio. The effects of increased temperature (32 °C) and decreased salinity (10 ppt) varied with test species. PFOS toxicity for the sheepshead minnows increased with temperature but was not altered by decreased salinity. For grass shrimp and mud snails, PFOS toxicity was greater under lower salinity. The combination of higher temperature and lower salinity was observed to lower the toxicity thresholds for all species. These data demonstrate that expanding toxicity testing to include a wider range of parameters will improve the environmental risk assessment of chemical contaminants, especially for species inhabiting dynamic estuarine ecosystems.

Evaluation of confirmatory data following the Article 12 MRL review for myclobutanil.

The applicant Corteva Agriscience submitted a request to the competent national authority in Austria to evaluate the confirmatory data that were identified for myclobutanil in the framework of the MRL review under Article 12 of Regulation (EC) No 396/2005 as not available. To address the data gap related to the lack of information on the triazole derivative metabolites (TDMs), new residue trials analysing for TDMs were submitted on apples, grapes, strawberries, tomatoes and melons. Following the assessment of the submitted data, EFSA concluded that Article 12 confirmatory data gaps are considered addressed for pome fruits, grapes, cucurbits with inedible peel, strawberries and tomatoes. The new information provided required the assessment of consumer exposure to TDMs, which identified no consumer intake concerns for the crops under consideration. No information was provided to address the Article 12 confirmatory data referred to in Regulation (EU) 2020/770 for blackberries, gooseberries, bananas, aubergines/eggplants, lamb's lettuces/corn salads, beans (with pods), globe artichokes, hops, sugar beet roots and products of animal origin. For these commodities, the existing EU MRL could be lowered to the enforcement limit of quantification (LOQ). For kaki/Japanese persimmon and azararoles/mediterranean medlars, the existing EU MRL is set on the basis of Codex MRL in pome fruits. The applicant did not request maintaining a Codex MRL in these commodities, but should risk managers decide otherwise, the Article 12 data gap is considered addressed for kaki/Japanese persimmon while for azaroles/Mediterranean medlars, a risk management decision might be required. No consumer intake concerns were identified.

Incidence of typhoid fever in Burkina Faso, Democratic Republic of the Congo, Ethiopia, Ghana, Madagascar, and Nigeria (the Severe Typhoid in Africa programme): a population-based study

Typhoid Fever remains a major cause of morbidity and mortality in low-income settings. The Severe Typhoid in Africa programme was designed to address regional gaps in typhoid burden data and identify populations eligible for interventions using novel typhoid conjugate vaccines. A hybrid design, hospital-based prospective surveillance with population-based health-care utilisation surveys, was implemented in six countries in sub-Saharan Africa. Patients presenting with fever (≥37·5°C axillary or ≥38·0°C tympanic) or reporting fever for three consecutive days within the previous 7 days were invited to participate. Typhoid fever was ascertained by culture of blood collected upon enrolment. Disease incidence at the population level was estimated using a Bayesian mixture model. 27 866 (33·8%) of 82 491 participants who met inclusion criteria were recruited. Blood cultures were performed for 27 544 (98·8%) of enrolled participants. Clinically significant organisms were detected in 2136 (7·7%) of these cultures, and 346 (16·2%) Salmonella enterica serovar Typhi were isolated. The overall adjusted incidence per 100 000 person-years of observation was highest in Kavuaya and Nkandu 1, Democratic Republic of the Congo (315, 95% credible interval 254-390). Overall, 46 (16·4%) of 280 tested isolates showed ciprofloxacin non-susceptibility. High disease incidence (ie, >100 per 100 000 person-years of observation) recorded in four countries, the prevalence of typhoid hospitalisations and complicated disease, and the threat of resistant typhoid strains strengthen the need for rapid dispatch and implementation of effective typhoid conjugate vaccines along with measures designed to improve clean water, sanitation, and hygiene practices. The Bill & Melinda Gates Foundation.

Harmonization of three different accelerometers to classify the 24 h activity cycle

Increasing interest in measuring key components of the 24 h activity cycle (24-HAC) [sleep, sedentary behavior (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA)] has led to a need for better methods. Single wrist-worn accelerometers and different self-report instruments can assess the 24-HAC but may not accurately classify time spent in the different components or be subject to recall errors. Objective. To overcome these limitations, the current study harmonized output from multiple complimentary research grade accelerometers and assessed the feasibility and logistical challenges of this approach. Approach. Participants (n = 108) wore an: (a) ActiGraph GT9X on the wrist, (b) activPAL3 on the thigh, and (c) ActiGraph GT3X+ on the hip for 7–10 d to capture the 24-HAC. Participant compliance with the measurement protocol was compared across devices and an algorithm was developed to harmonize data from the accelerometers. The resulting 24-HAC estimates were described within and across days. Main results. Usable data for each device was obtained from 94.3% to 96.7% of participants and 89.4% provided usable data from all three devices. Compliance with wear instructions ranged from 70.7% of days for the GT3X+ to 93.2% of days for the activPAL3. Harmonized estimates indicated that, on average, university students spent 34% of the 24 h day sleeping, 41% sedentary, 21% in LPA, and 4% in MVPA. These behaviors varied substantially by time of day and day of the week. Significance. It is feasible to use three accelerometers in combination to derive a harmonized estimate the 24-HAC. The use of multiple accelerometers can minimize gaps in 24-HAC data however, factors such as additional research costs, and higher participant and investigator burden, should also be considered.

Experimentally Validated Inverse design of Multi-Property Fe-Co-Ni alloys

This study presents a machine learning (ML) framework aimed at accelerating the discovery of multi-property optimized Fe-Ni-Co alloys, addressing the time consuming, expensive, and inefficient nature of traditional methods of material discovery, development, and deployment. We compiled a detailed heterogenous database of the magnetic, electrical, and mechanical properties of Fe-Co-Ni alloys, employing a novel ML-based imputation strategy to address gaps in property data. Leveraging this comprehensive database, we developed predictive ML models using tree-based and neural network approaches for optimizing multiple properties simultaneously. An inverse design strategy, utilizing multi-objective Bayesian optimization (MOBO), enabled the identification of promising alloy compositions. This approach was experimentally validated using high-throughput methodology, highlighting alloys such as Fe66.8Co28Ni5.2 and Fe61.9Co22.8Ni15.3, which demonstrated superior properties. The predicted properties data closely matched experimental data within 14% accuracy. Our approach can be extended to a broad range of materials systems to predict novel materials with optimized set of properties.

Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins

Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites using the daily nitrate concentration and stream discharge information of a neighboring high-frequency nitrate monitoring site. A Long Short-Term Memory (LSTM) based deep learning (DL) modeling framework was developed to predict daily nitrate concentrations. The DL modeling framework performance was compared with two well-established statistical models, including LOADEST and WRTDS-Kalman, in three selected basins in Iowa, USA: Des Moines, Iowa, and Cedar River. The developed DL model performed well with NSE >0.70 and KGE >0.70 for 67% and 79% nitrate monitoring sites, respectively. DL and WRTDS-Kalman models performed better than the LOADEST in nitrate concentration and load estimation for all low-frequency sites. The average NSE performance of the DL model in daily nitrate estimation is 20% higher than that of the WRTDS-Kalman model at 18 out of 24 sites (75%). The WRTDS-Kalman model showed unrealistic fluctuations in the estimated daily nitrate time series when the model received limited observed nitrate data (less than 50) for simulation. The DL model indicated superior performance in winter months' nitrate prediction (60% of cases) compared to WRTDS-Kalman models (33% of cases). The DL model also better represented the exceedance days from the USEPA maximum contamination level (MCL). Both the DL and WRTDS-Kalman models demonstrated similar performance in annual stream nitrate load estimation, and estimated values are close to actual nitrate loads.